v246: Proceedings of PGM 2024

2026-06-10 · Source: Proceedings of Machine Learning Research · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, medium

Summary

This volume, "PMLR 246", presents the proceedings of The 12th International Conference on Probabilistic Graphical Models, held from September 11-13, 2024, in Nijmegen, the Netherlands, edited by Johan Kwisthout and Silja Renooij. The papers cover a wide range of advancements in PGM research, including novel approaches to causal inference, such as alternative measures of direct/indirect effects, sensitivity analysis to unobserved confounding using normalizing flows, and automated causal discovery. Significant contributions address the learning and inference aspects of various graphical models, including efficient detection of commutative factors in factor graphs, variable order instability in structure learning, and Q-conjugate message passing for Bayesian inference. Furthermore, the proceedings explore specialized models like LIMID Quality Control Models, Latent Gaussian Graphical Models, and Probabilistic Circuits, alongside practical applications in areas such as shared decision-making, psychometric modeling, argument mining for city project proposals, and analyzing conspiracy theories.

Key takeaway

The 12th International Conference on Probabilistic Graphical Models (PGM 2024) presents significant advances in causal inference, model learning, and efficient inference techniques. Contributions include novel methods for sensitivity analysis to unobserved confounding (ρ-GNF), automated causal discovery (AutoCD), and optimized algorithms for factor graphs, arc-reversal, and MPE queries on tensor networks. This volume offers critical insights and tools for researchers and practitioners tackling complex problems in AI, machine learning, and decision-making under uncertainty.

Topics

• Causal Inference
• Bayesian Networks
• Structure Learning
• Normalizing Flows
• Bayesian Inference

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Proceedings of Machine Learning Research.